2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Preliminary, please do not cite
Comparing Apples with….Apples:
How to Make (More) Sense of Subjective Rankings of Constraints to Business
Mary Hallward-Driemeier and Reyes Aterido
The World Bank*
November 2006
Abstract: The use of expert surveys and subjective measures to rank countries or potential
constraints is widespread. However, the use of such data is subject to well known
limitations, including biases stemming from the representativeness of respondents,
differences across respondents in yardsticks used, to differences in the ‘optimism’ of
respondents. But testing for the importance of these potential limitations is itself too often
constrained by a lack of suitable data. The robustness of subjective responses can be tested
using the Investment Climate Enterprise Survey data that has comparable data on 50,000
firms in 75 countries. The data includes the subjective ratings of constraints to operating
their businesses as reported by managers. But the survey is unique in two aspects. It also
includes objective measures of how these constraints are experienced in practice, e.g. the
time and monetary costs of complying with regulations, actually loses from power outages
or crime etc. And it can directly relay these measures to information on the firm’s own
performance.
The analysis shows that the perception data actually performs well once the ratings
are converted into a relative rather than absolute scale -- or when country dummies are
included so that the variation being exploited is within country. Relative rankings are
indeed correlated with the objective measures – both from the survey and from outside
sources. The analysis also shows that views are not simply expressions of a firm’s own
performance. However, whether a firm is expanding or contracting can affect the relative
importance of certain constraints – particularly in areas such as finance, labor regulations
and corruption. These findings raise the information requirements in interpreting
subjective rankings, but give greater confidence in their findings and give greater insights
into the types of firms that would benefit from different reforms.
*
The opinions are solely those of the authors and do not necessarily reflect the official views of the World
Bank or its Executive Directors.
June 24-26, 2007
Oxford University, UK
1
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
In Brazil, 80 percent of managers have ranked access to finance as a major or
severe constraint. But in Kazakhstan just under 20 percent of managers have. Is access to
finance really so much worse in Brazil than in Kazakhstan?
Not necessarily. The interpretation of these subjective responses can change
substantially with additional information that is available. In Brazil, 17 of the 18 possible
constraints are identified as major or severe by a higher proportion of respondents than
identify the top constraint as major or severe in Kazakhstan. Switching from an absolute
ranking to a relative one, finance is reported as the top constraint in both countries.
Looking at more objective information on access to finance, only 13% of small firms in
Kazakhstan are able to access formal external finance, while half of the small firms in
Brazil are able to. Firms that are able to access such finance tend to be more productive,
and productivity levels are higher on average in Brazil.
So, comparisons are more meaningful when they are based on relative rather than
absolute rankings and are linked to information on objective measures of constraints and
respondents’ own performance.
How well do subjective ratings capture the relative strengths and weaknesses of the
business environment in which the respondents work? The Investment Climate Enterprise
Surveys (IC-ES) provide a unique way of testing how well subjective responses reflect
objective conditions, as well as the extent to which they are influenced by the respondent’s
own performance.
In the IC-ES, one set of questions ask managers for their perceptions of how
constraining various dimensions of the investment climate are to the operation and growth
of their business. These subjective measures, usually asked on a scale of 0 “no problem” to
4 “severe constraint”, are popular as they give a quick synopsis of constraints and are
assumed to be easy to interpret.
However, comparisons of these questions across individuals – let alone countries—
are not straightforward as the italicized paragraph above indicates. There are several
potential pitfalls in making such comparisons. However, some of them can be addressed,
particularly if additional information about the respondents is available. So what is of
interest here is to take advantage of the broader survey to explore ways that one can
improve the scope for comparisons and to gauge how well the subjective information
actually reflects investment climate conditions.
This paper pursues this question using three approaches:
 First, it looks at the internal consistency of responses. It links the subjective
perceptions of possible constraints with more objective responses. For
example, it looks at whether firms that experience more power outages are
more likely to complain that electricity is a problem.
June 24-26, 2007
Oxford University, UK
2
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3

Second, it examines the links between responses and performance. It tests
that the ratings reflect the business environment rather than the business’
performance. And it examines the extent to which differences in the
business environment could actually vary by firm performance (i.e. do more
productive firms or firms that are growing actually experience better or
worse conditions than their more stagnant counterparts?)

Third, it links the measures of investment climate from the surveys with
other data sources (usually only available at the country level).
The analysis shows that the perception data actually performs well once the ratings
are converted into a relative rather than absolute scale. Relative rankings are indeed
correlated with the objective measures – both from the survey and from outside sources.
The analysis also shows that views can reflect some indicators of a firm’s own
performance. However, controlling for a firm’s performance does not systematically alter
the relative rankings based on other characteristics such as size, ownership or export
orientiation. The one exception is actually the one where firm performance should matter,
namely access to external finance. Here, a firm’s performance is indeed both a predictor of
how easy they report it is to get access to finance as well as whether firms actually receive
external finance. These results have implications both for how subjective variables are
evaluated, as well as indicating the extent to which endogeneity is a concern in studies that
seek to use subjective measures to explain differences in firm performance (see Dollar,
Hallward-Driemeier, Mengistae 2005 and Carlin, Schaffer and Seabright 2004 for different
approaches to this issue.)
The paper is organized as follows. The next section elaborates on the challenges of
using perception data to make comparisons across respondents. Section 3 discusses the ICES data. Section 4 looks at how well subjective rankings relate to objective measures.
Section 5 looks at how robust these findings are to the inclusion of firm performance
measures. Section 6 then looks to see if firm performance itself matters for the objective
quality of the investment climate. Section 7 concludes.
II: Challenges of using perception data:
Interest in being able to benchmark conditions across locations is strong – and
growing. The most common means of doing this rely on the perceptions of experts or on
broader surveys of people in the locations. Media outlets are full of their own polls or
those by polling experts such Gallup or Pew Center, that report the perceptions of people
on a variety of topics related to business conditions. The World Economic Forum’s Global
Competitiveness Report uses subjective ratings to rank countries, as does the ICRG and the
Heritage’s Index of Economic Freedom.
Clearly perceptions do matter and are of inherent interest, particularly in
understanding the factors affecting a number of forward-looking decisions such as
decisions to invest, train, hire new workers, enter new markets or upgrade products (World
Development Report 2005). What is looked at here is how well these perceptions reflect
reality and conditions beyond the firm’s own performance. The richness of the investment
June 24-26, 2007
Oxford University, UK
3
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
climate data as well as sources of other data allows one to test how well these perceptions
fare.
Despite the widespread use and acceptance of using subjective data to rank
countries, there are many limitations inherent in using these measures. Potential
shortcomings in comparing subjective responses include:
1. Optimism or kvetch factor. This is typified by the example of whether a respondent
sees the glass as half empty or half full. Some people like to complain more than
others. Some are more optimistic than others, rarely complaining about anything.
Alternatively, people may agree that a particular issue is a problem, but the same
experience or condition may be rated as ‘severe’ by one person while it may be
‘moderate’ or ‘major’ to another. Thus differences in responses may reflect the degree
of optimism rather than actual differences in the underlying investment climate
conditions. This effect can be at work across individuals, but is it also striking across
countries where some cultures are more or less willing to report that potential obstacles
are constraining. The ‘optimism/kvetch’ factor is likely to shift all of an individual’s
responses up or down, but less likely to affect the relative rankings between obstacles
(see D. Kaufman and H. Broadman; J. Svensson).
2. Reference point bias: people may have different expectations against which something
is being measured. If the expectations are that something is always available, then even
brief or infrequent interruptions may get rated as a ‘major’ or ‘severe’ constraint –
compared to a location where interruptions are commonplace and assumed to be normal
and so rated as ‘minor’. Most ratings are made in comparison with something else –
and the more explicit the ‘something else’ is, the more consistent the ratings are likely
to be.
3. Performance bias—whether ratings actually reflect the environment in which the firm
operates rather than the firm’s performance in the environment.
a. Note, this could go either way – firms that are doing poorly may ‘blame’
the investment climate, increasing their ratings of the difficulty of doing
business even if the reason for their lack of success is independent of the
investment climate. But, it may also be that it is precisely those firms
that are doing well and trying to expand that may complain more,
finding that weak investment climate conditions really are constraining
them. So it is a matter of interest to know not just if there are
performance biases, but also in with direction they might be working.
(One area of particular concern regards interactions with government
officials and the potential for corruption. For example, corrupt officials
may target growing firms as they have a greater ability to pay and are
more likely to be on the radar screen. See Daniel Kaufmann; Jakob
Svensson).
b. Lagged performance values are used as controls. To the extent they are
significant, the results point to the need to control for endogeneity in
June 24-26, 2007
Oxford University, UK
4
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
studies that seek to use subjective rankings of constraints to explain
differences in firm performance.
4. Do respondents really differentiate between obstacles: It is possible that respondents
don’t differentiate much between the various potential issues they are asked to rate. If
people are thinking more about the overall business environment, they may respond as
if each obstacle is a proxy for the larger set of conditions. Or they may express their
frustration with overall conditions by blaming all potential sources equally. Did the
respondent take the time to differentiate which of the potential obstacles really were
more problematic than others?
5. The ordering of constraints may also matter: earlier elements in the list may get higher
or lower rankings, more care may be taken to differentiate between them or the
comparison may be made with the obstacle right before the current one so that
comparisons between two further apart in the list might be different if they were asked
right after each other.
How important are these potential shortcomings?
The richness of the data in the survey (as well as the ability to link it to other data
sources) allows for many of these potential weaknesses to be addressed. A first step is to
look at relative rather than absolute rankings. Thus, the rankings are adjusted by
subtracting that firm’s average of all the other scores on the list. This not only controls for
the manager’s optimism/kvetch factor, it also removes the effect of any characteristics or
location-specific circumstance that affects all the perceptions. This demeaning does not
allow for the comparisons of the level of constraints, but does look at the relative
importance of a particular constraint compared to the others. The remaining relative
ranking of constraints is still a valid means of capturing investment climate problems.
This step can also go a long way in addressing concern 2, difference reference
points, so long as these effects work to shift all responses together. But it still raises the
question of whether subjective measures correspond well to more objective measures. This
dataset provides an ideal means of testing for this. In the results discussed below, the
subjective measures of the investment climate are correlated with differences in more
objective measures that should underlie them (and controlling for differences in firm
characteristics).
In terms of a potential performance bias, the data can be used to test for this, and if
necessary, firm performance can be controlled for in making comparisons. The tests of
issues 2 and 3 are the bulk of the paper and so are discussed in greater detail below.
The fourth potential concern, insufficient variation in rankings, is not an issue here.
The data shows that there is considerable variation across the list of potential obstacles
within a single firm’s responses. Only 3 percent give the same response to all the issues –
and of these 3 percent, almost 90 percent of them given a consistent ‘no problem’ rating
and these firms are predominantly located in OECD countries. Two-thirds (68%) of firms
use the top rating in ranking their most binding constraint. There are some obstacles that
share the firm’s top rank, but there is still strong evidence of differentiation. 95 percent of
firms rank 4 or less of the possible 18 obstacles with their top rating. A quarter of firms
June 24-26, 2007
Oxford University, UK
5
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
report only one issue with their top rating. Over a third use all the ratings available across
the 18 issue areas.
The last potential concern, that the ordering of constraints may matter, could be
addressed in a field experiment looking to see how responses change with alternative
orderings of the list. It cannot be tested for here as the same ordering was used
everywhere. However, the consistency in the ordering does imply that whatever effect it
has, it should be the same across respondents.
Data description
The paper uses firm level data from the World Bank’s Investment Climate
Enterprise Surveys. Face to face interviews have been conducted with over 50,000 firms in
75 countries. The survey collects detailed information on many aspects of the investment
climate in which a firm operates as well as information about the firm’s own performance.
The same questionnaire is implemented in each country, with a standardized sampling
methodology, making the data comparable across countries. Questions include both
subjective questions on various potential constraints as well as more objective measures,
such as the time and monetary costs of completing various transactions or accessing
services. This paper draws on both types of information. Measures of firm performance
include sales growth, employment growth, productivity and investment patterns. (See
Hallward-Driemeier and Iarossi 2006 for more details on the dataset.)
1.
How well do subjective responses reflect objective conditions?
We have an ideal dataset that includes both objective and subjective measures of the
investment climate to empirically analyze the question “how well subjective reflect
objective conditions”. We test the following model:
IC_subjectiveit=ß1*IC_objectiveit + ß2*Performance(t-1)i + ∑ ßk*Xkit +eit (1)
Note1: X is a set of dummy variables identifying firms’ characteristics such as size, firm age, dummy if
located in capital city, ownership (foreign and government), exporter, industry (11 sectors), and country
Note2: We run the model 4 times for each pair of subjective-objective variables at time t1 and include one
different performance variable in each regression: 1. employment growth period t3-t2; employment growth
t2-t1; investment t2; and log (TFP) t2.
Note3: The performance variables are all lagged one period due to concerns of potential endogeneity.
Note4: We check robustness by including separately education and experience of the manager.
We find strong empirical evidence that perceptions reflect the real investment climate.
Thus, ß1 is significant and with the appropriate sign in 10 different dimensions of the
investment climate. It should be noted that these results hold for both the subjective IC
variable as a relative measure (i.e. the perception of constraint i minus the average of all
other constraints by the same respondent) as well as for the absolute level of constraints –
if country dummies are included. Thus, it appears that while individual managers may
‘kvetch’ more than others, there is a stronger cultural effect; that the average level of
June 24-26, 2007
Oxford University, UK
6
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
complaint tends to be more similar within a country. For cross-country comparisons, the
relative rankings are easier to interpret.
The lagged-performance variable controls for the fact that good or bad performance may
affect perceptions as well as the objective IC variable if this implies officials’ decisions.
Equation (1) measures the impact of objective conditions on perceptions when all firms’
characteristics and output level are equal. In addition, we test results controlling first for
manager’s education and then for manager’s experience, and conclude that results are
robust.
The ten dimensions of the perceptions about the investment climate under which there is
empirical evidence of this result are the following themes:
1. tax administration,
2. obtaining licenses and permits,
3. telecommunications,
4. electricity,
5. customs,
6. finance,
7. corruption,
8. crime,
9. consistency of regulations,
10. confidence in property rights
In terms of linking these subjective rankings with more objective data, the following
variables were used:
1. tax administration - time spent with and gifts given to officials for tax purposes;
2. business licenses and operating permits -- number of days to obtain a license
3. telecommunications -- length of waiting time to get a phone line;
4. electricity -- days of power outage and losses1 due to lack of electricity;
5. customs -- time necessary to be able to claim imports;
6. access to finance -- days that takes to clear a check and share of financing from formal
external sources;
7. corruption – size and frequency of bribes, gifts incurred during inspections and
government contracts;
8. crime -- security costs, losses due to crime and the percentage of crimes reported to the
authorities.
9. consistency of regulations -- manager’s time spent with officials
10. property rights -- time to solve overdue payments in courts;
Table 1 reports the ß1 coefficient and p-value corresponding to ic_objectiveit in equation (1).
Each row represents a separate regression, with the full set of controls indicated at the
bottom of the table.
Some other interesting facts stemming from this equation are:
1
This result prevails after controlling for whether the firm has a generator
June 24-26, 2007
Oxford University, UK
7
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
o
complaints about customs are driven by the waiting time of being able to claim
imports, from the time they arrive to the entry point, but less so by delays on processing
exports through customs. In all regions, delays on exports are shorter than delays on
imports and they do not seem to be as much of a concern. We test this in a sample
conditioned to exporters only and results prevail.
o
financial "cost" is a greater obstacle than "access". Complaints on both access-to
and cost-of finance increase the poorer is the firm’s performance in the previous period. If
complaints on access/cost of finance are "due" to Banks' lending decisions rather than to
financial conditions, regressions with overdraft in the LHS and performance in the RHS
would show that "lower performance" causes "lower credit". Investment seems to boost
credit (not TFP or employment growth), therefore we don’t find evidence of Bank’s
lending favoring more productive firms. The decision to invest implies that the firm has
access to finance and not the other way around.
2. How much does a firm’s performance color its view of constraints?
The results show that a firm’s performance can have an impact, but there are not
consistent effects across all dimensions of the investment climate. Table 2 here shows the
impact based on a firm’s employment growth. The regressions include dummies for firms
that are expanding and those that are contracting, with the omitted group those that have a
stable labor force.
The most striking finding is that firms that are adjusting face rank constraints
significantly differently from firms that are stable. This is most pronounced for
employment growth (similar results for investment, sales growth and productivity have
been calculated and are available upon request).
Second, for some constraints, the signs on the expanding and contracting dummies
are the same. But for some they are different. Thus, for finance – it is less of a constraint
for expanding firms but significantly more of a constraint for contracting firms.
Furthermore, firms with overdraft or credit line complaint about cost-of but not access-to
finance. The opposite pattern is true for regulatory burdens and skills shortages where
expanding firms report greater relative constraints.
Firms that invested in the previous period complain more about electricity. This
reflects that electricity failures are a hindrance to investment returns. Employment growth
also seems to put off complaints about crime. This could reflect that concerns about
security may refrain hiring. We test this and find out that firms that are hiring and
expanding are paying less for security – a more secure location boosts growth.
While these results show that the performance of the respondent can affect the
relative rankings of constraints, what remains robust is the effect of other firm
characteristics on perceptions. Thus, smaller firms or exporting firms have different
priorities for reform – and these results remain significant even controlling for firm
performance measures. [This section is being expanded]
3. Are objective measures themselves affected by a firm’s performance?
We address this by running a set of regressions with ic_objective in the LHS and lagged
performance in the RHS. The variables we are concerned with are those with potential to
affect officials’ behavior such us bribes, inspections, manager time, and delays in several
dimensions. Table 3 summarizes results from these regressions (equation 2)
June 24-26, 2007
Oxford University, UK
8
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
IC_objectiveit = ß1*Performance(t-1)i + ∑ ßk*Xkit +eit (2)
Our results support that: (i) manager time is in fact longer for more productive firms
(measured by sales growth and investment) indicating that government officials are more
tuned to productive firms and suggesting that they may obtain gains from it; (ii) however,
different measures of corruption offer inconclusive results. While investment seems to
attract bribes and gifts at inspections, none of the other performance variables have an
effect on corruption and on the contrary, the percentage of gifts on contracts is lower for
more productive firms other things equal; (iii) other dimensions of the investment climate
such as infrastructure do not appear to be a function of firm performance – with the one
exception of firm investment and electricity outages. This would be consistent with
investments including purchases of generators (a hypothesis directly tested in Uganda, see
Svensson).
Summarize main messages (to be further developed):
The subjective measures are good reflections of objective conditions. However,
care still needs to be taken in how to interpret them. In particular, there does appear to be
differences in optimism or a willingness to complain that acts to shift constraints up or
down. While there is some evidence of this at the firm level, the effect appears to be larger
across countries. Thus, comparisons across countries should look at relative rankings of
constraints rather than absolute rankings. If data is pooled across countries, country
dummies should be included so that the variation being exploited is within country.
Finally, whether a firm is contracting or expanding can affect the relative importance of
different constraints so that the respondent’s performance should be taken into account in
assessing the priorities for reforms.
[Will elaborate on the substantive messages of which constraints matter more]
Extensions:
While average effect of an IC measure may not be significant overall, it can be significant
for sub-groups of firms (e.g. by size) and effects could be non-linear.
June 24-26, 2007
Oxford University, UK
9
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
Table 2
Do Subjective Rankings Reflect Objective Conditions?
contraint
objective
1. tax administration
days tax inspections
tax administration
gift to officials during tax inspections
tax administration
% sales reported for taxes
2. obtaining licenses and permits
lgdysoplicen
3. telecommunications
use of email with clients
telecommunications
days to connect a phone line
4. electricity
lgdysnopower
electricity
loss % sales due to lack power
electricity
days power outages
5. customs
days imports through customs
customs
days exports through customs
6. access finance
share sales on credit
access finance
overdraft facility
cost finance
share sales on credit
devobscostfin
overdraft facility
7. corruption
bribe (% sales)
corruption
bribe (yes-no)
corruption
gift to officials in inspections (yes-no)
corruption
gift to officials (% government contracts)
8. crime
cost security (%sales)
crime
losses due to crime (%sales)
crime
number of crime reported
9. regulations
manager time with officials
regulations
% sales reported for taxes
10.confidence in property rights*
share sales on credit
confidence in property rights*
weeks to solve conflict in courts
coefficient
0.003
-0.107
-0.001
0.088
-0.065
0.068
0.201
0.018
0.203
0.062
0.037
-0.001
-0.051
0.000
0.109
0.005
0.323
0.237
0.005
0.003
0.010
0.002
-0.006
0.001
0.001
-0.005
p-value
0.006
0.004
0.014
0.000
0.005
0.000
0.000
0.000
0.000
0.000
0.109
0.048
0.099
0.058
0.000
0.006
0.000
0.000
0.000
0.028
0.004
0.000
0.000
0.046
0.017
0.000
* larger number more confidence
All regressions are controlled by: performance lagged, size, age, location, export activity, foreign ownership,
governent own, sector, country
June 24-26, 2007
Oxford University, UK
10
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
TABLE 2
DEPENDENT VARIABLE: RELATIVE PERCEIVED CONSTRAINTS (deviation from mean)
(1)
(4)
(5)
(7)
(8)
(9)
(10)
mean constraints telecommuni electricity
cations
(2)
(3)
finance
transportation corruption
regulatory
labor
regulations
skills
shortage
uncertainty
Expanding firms
0.118***
(0.012)
-0.028*
(0.016)
-0.056***
(0.021)
-0.004
(0.016)
0.012
(0.017)
0.094***
(0.018)
0.042***
(0.015)
0.121***
(0.017)
-0.018
(0.019)
Contracting firms
0.115***
(0.016)
-0.066*** -0.094***
(0.022)
(0.024)
0.243***
(0.027)
-0.091***
(0.021)
0.013
(0.023)
-0.158*** 0.007
(0.024)
(0.021)
-0.029
(0.023)
0.111***
(0.025)
-0.056***
(0.017)
Constant
(6)
0.435***
-0.565*** 0.259*** 0.429***
-0.512*** 0.650*** 0.395*** -0.566*** -0.464***
(0.044)
(0.077)
(0.081)
(0.118)
(0.078)
(0.103)
(0.084)
(0.128)
(0.113)
Observations
45435
44526
44761
42444
44092
43247
40301
43731
44248
R-squared
0.28
0.14
0.23
0.14
0.12
0.13
0.11
0.16
0.11
Size dummies
YES
YES
YES
YES
YES
YES
YES
YES
YES
Age dummies
YES
YES
YES
YES
YES
YES
YES
YES
YES
Location dummies
YES
YES
YES
YES
YES
YES
YES
YES
YES
Exporter dummy
YES
YES
YES
YES
YES
YES
YES
YES
YES
Foreign owned dummy YES
YES
YES
YES
YES
YES
YES
YES
YES
Sector dummies
YES
YES
YES
YES
YES
YES
YES
YES
YES
Country dummies
YES
YES
YES
YES
YES
YES
YES
YES
YES
Robust standard errors in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Expansion and contraction dummies are based on employment, with the missing category as those firms with stable levels of employment.
June 24-26, 2007
Oxford University, UK
11
-0.381***
(0.084)
42164
0.15
YES
YES
YES
YES
YES
YES
YES
2007 Oxford Business & Economics Conference
ISBN : 978-0-9742114-7-3
TABLE 3
Do Investment Climate Objective Conditions Vary by Firm Performance?
coefficient
p-value
Objective investment climate variable
Lagged performance
Licenses and permits
Days to obtain licenses
Customs
Days to clear customs: exports
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
0.0154
0.0193
0.0001
-0.0612
0.0577
-0.0088
0.0237
-0.0001
-0.0008
-0.0351
0.0174
-0.0066
0.0000
-0.0790
-0.0040
0.0015
-0.0525
0.0001
0.0031
0.0748
0.0061
0.0186
-0.0001
0.0293
0.0446
0.0166
0.2949
0.0004
-0.0103
-0.0111
0.4247
0.9003
0.7802
0.5162
0.2857
0.5917
0.7625
0.6286
0.9820
0.2565
0.1747
0.9310
0.9658
0.0329
0.8939
0.9295
0.4089
0.6373
0.9189
0.0342
0.1802
0.2853
0.4206
0.0012
0.0000
0.5477
0.0012
0.2629
0.8229
0.7544
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
0.2112
-1.2285
0.0186
1.4429
3.1706
0.0079
0.0015
0.0001
0.0084
0.0617
0.7042
0.4507
0.0060
0.0767
0.0000
0.0263
0.9378
0.3638
0.4004
0.0000
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
0.0015
-0.5102
0.0017
-0.5283
0.2725
0.0020
0.0123
0.0001
-0.0028
0.0458
0.0083
-0.0145
-0.0001
0.0006
0.0305
0.0172
-1.4755
-0.0035
-0.6620
0.2458
0.9528
0.3468
0.4002
0.0177
0.2530
0.7967
0.6665
0.4798
0.8368
0.0011
0.0166
0.5163
0.1382
0.9561
0.0075
0.7526
0.0030
0.0578
0.0112
0.2830
Days to clear customs: imports
Electricity
Days with power outages
Have own generator
Telecommunications
Finance
Days to get new phone line
Sales sold on credit
Have bank overdraft
Corruption
Bribes paid 'to get things done'
Frequency of bribes
Gifts at inspections
Gifts to win contracts
**
**
***
***
***
***
*
***
**
***
**
***
**
***
***
*
**
Consistency of regulations
Management time with officials
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
0.0064
-0.8307
0.0083
0.0261
0.6875
0.9504
0.2093
0.0025 ***
0.9311
0.0575 *
Crime
Costs of security
Employment Growth1
Employment Growth2
Sales Growth
TFP
Invest
Employment Growth1
Employment Growth2
Sales Growth
TFP
-0.0559
-0.2547
0.0011
0.5430
0.2481
-0.0378
-0.0380
-0.0024
-0.2542
0.3618
0.3133
0.4691
0.0186 **
0.1137
0.5601
0.8394
0.0109 **
0.0089 ***
Losses from crime
June 24-26, 2007
Oxford University, UK
12